Share：
Share this content in WeChat
X
[Chinese][PDF]77236
• Review •
The application progress of artificial intelligence in gastric cancer imaging
LIU Bo  LIU Fei  ZHOU Guanzhi  ZHANG Dengyun  WANG Hexiang  WANG He  ZHANG Qun  ZHANG Jian 

Cite this article as: Liu B, Liu F, Zhou GZ, et al. The application progress of artificial intelligence in gastric cancer imaging[J]. Chin J Magn Reson Imaging, 2022, 13(6): 155-159. DOI:10.12015/issn.1674-8034.2022.06.033.


[Abstract] Gastric cancer (GC) is one of the most common cancers and one of the leading causes of cancer-related death in China. The non-invasive accurate diagnosis is fundamental to optimal therapeutic decision-making. Artificial intelligence (AI) techniques, particularly radiomics and deep learning, have brought new research hotspots in interdisciplinary of imaging and gastric cancer diagnosis and treatment. AI has been used widely in GC research, because of its ability to convert medical images into minable data and to detect invisible textures. In this article, we systematically reviewed the methodological processes and current clinical applications involved in AI. Challenges and opportunities in AI-based GC research are highlighted.
[Keywords] artificial intelligence；gastric cancer；radiomics；deep learning；magnetic resonance imaging

LIU Bo1   LIU Fei2   ZHOU Guanzhi1   ZHANG Dengyun1   WANG Hexiang2   WANG He1   ZHANG Qun3   ZHANG Jian1*  

1 Department of Gastrointestinal Surgery, the Affiliated Hospital of Qingdao University, Qingdao 266700, China

2 Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266003, China

3 The Institute of High Energy Physics of the Chinese Academy of Sciences, Beijing 100049, China

Zhang J, E-mail: zhangjian@qduhospital.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81770631).
Received  2022-02-25
Accepted  2022-05-27
DOI: 10.12015/issn.1674-8034.2022.06.033
Cite this article as: Liu B, Liu F, Zhou GZ, et al. The application progress of artificial intelligence in gastric cancer imaging[J]. Chin J Magn Reson Imaging, 2022, 13(6): 155-159. DOI:10.12015/issn.1674-8034.2022.06.033.

[1]
Xia CF, Dong XS, Li H, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants[J]. Chin Med J (Engl), 2022, 135(5): 584-590. DOI: 10.1097/CM9.0000000000002108.
[2]
Global Advanced/Adjuvant Stomach Tumor Research International Collaboration Group. Benefit of adjuvant chemotherapy for resectable gastric cancer: a meta-analysis[J]. JAMA, 2010, 303(17): 1729-1737. DOI: 10.1001/jama.2010.534.
[3]
Wang FH, Shen L, Li J, et al. The Chinese Society of Clinical Oncology (CSCO): clinical guidelines for the diagnosis and treatment of gastric cancer[J]. Cancer Commun (Lond), 2019, 39(1): 10. DOI: 10.1186/s40880-019-0349-9.
[4]
Seevaratnam R, Cardoso R, McGregor C, et al. How useful is preoperative imaging for tumor, node, metastasis (TNM) staging of gastric cancer? A meta-analysis[J]. Gastric Cancer, 2012, 15(Suppl 1): S3-S18. DOI: 10.1007/s10120-011-0069-6.
[5]
Li R, Li J, Wang XP, et al. Detection of gastric cancer and its histological type based on iodine concentration in spectral CT[J]. Cancer Imaging, 2018, 18(1): 42. DOI: 10.1186/s40644-018-0176-2.
[6]
Lee DH, Kim SH, Joo I, et al. CT Perfusion evaluation of gastric cancer: correlation with histologic type[J]. Eur Radiol, 2018, 28(2): 487-495. DOI: 10.1007/s00330-017-4979-5.
[7]
Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications[J]. CA Cancer J Clin, 2019, 69(2): 127-157. DOI: 10.3322/caac.21552.
[8]
Jiang YM, Chen CL, Xie JJ, et al. Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer[J]. EBioMedicine, 2018, 36: 171-182. DOI: 10.1016/j.ebiom.2018.09.007.
[9]
Jiang YM, Wang W, Chen CL, et al. Radiomics signature on computed tomography imaging: association with lymph node metastasis in patients with gastric cancer[J]. Front Oncol, 2019, 9: 340. DOI: 10.3389/fonc.2019.00340.
[10]
Dong D, Fang MJ, Tang L, et al. Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study[J]. Ann Oncol, 2020, 31(7): 912-920. DOI: 10.1016/j.annonc.2020.04.003.
[11]
Gao XJ, Ma TT, Bai S, et al. A CT-based radiomics signature for evaluating tumor infiltrating Treg cells and outcome prediction of gastric cancer[J]. Ann Transl Med, 2020, 8(7): 469. DOI: 10.21037/atm.2020.03.114.
[12]
Gao XJ, Ma TT, Cui JL, et al. A radiomics-based model for prediction of lymph node metastasis in gastric cancer[J]. Eur J Radiol, 2020, 129: 109069. DOI: 10.1016/j.ejrad.2020.109069.
[13]
Zhang WJ, Fang MJ, Dong D, et al. Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer[J]. Radiother Oncol, 2020, 145: 13-20. DOI: 10.1016/j.radonc.2019.11.023.
[14]
Chen XF, Yang ZQ, Yang JD, et al. Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study[J]. Cancer Imaging, 2020, 20(1): 24. DOI: 10.1186/s40644-020-00302-5.
[15]
Wang Y, Liu W, Yu Y, et al. Potential value of CT radiomics in the distinction of intestinal-type gastric adenocarcinomas[J]. Eur Radiol, 2020, 30(5): 2934-2944. DOI: 10.1007/s00330-019-06629-3.
[16]
Gao XJ, Ma TT, Cui JL, et al. A CT-based radiomics model for prediction of lymph node metastasis in early stage gastric cancer[J]. Acad Radiol, 2021, 28(6): e155-e164. DOI: 10.1016/j.acra.2020.03.045.
[17]
Li Q, Qi L, Feng QX, et al. Machine learning-based computational models derived from large-scale radiographic-radiomic images can help predict adverse histopathological status of gastric cancer[J]. Clin Transl Gastroenterol, 2019, 10(10): e00079. DOI: 10.14309/ctg.0000000000000079.
[18]
Li WC, Zhang LW, Tian C, et al. Prognostic value of computed tomography radiomics features in patients with gastric cancer following curative resection[J]. Eur Radiol, 2019, 29(6): 3079-3089. DOI: 10.1007/s00330-018-5861-9.
[19]
Polanski WH, Zolal A, Sitoci-Ficici KH, et al. Comparison of automatic segmentation algorithms for the subthalamic nucleus[J]. Stereotact Funct Neurosurg, 2020, 98(4): 256-262. DOI: 10.1159/000507028.
[20]
Kumaraswamy AK, Patil CM. Automatic prostate segmentation of magnetic resonance imaging using Res-Net[J]. MAGMA, 2021: 2021Dec10. DOI: 10.1007/s10334-021-00979-0.
[21]
Zhou XR. Automatic segmentation of multiple organs on 3D CT images by using deep learning approaches[J]. Adv Exp Med Biol, 2020, 1213: 135-147. DOI: 10.1007/978-3-030-33128-3_9.
[22]
Zhang YT, Li HM, Du J, et al. 3D multi-attention guided multi-task learning network for automatic gastric tumor segmentation and lymph node classification[J]. IEEE Trans Med Imaging, 2021, 40(6): 1618-1631. DOI: 10.1109/TMI.2021.3062902.
[23]
Li HM, Liu B, Zhang YT, et al. 3D IFPN: improved feature pyramid network for automatic segmentation of gastric tumor[J]. Front Oncol, 2021, 11: 618496. DOI: 10.3389/fonc.2021.618496.
[24]
Gao Y, Zhang ZD, Li S, et al. Deep neural network-assisted computed tomography diagnosis of metastatic lymph nodes from gastric cancer[J]. Chin Med J (Engl), 2019, 132(23): 2804-2811. DOI: 10.1097/CM9.0000000000000532.
[25]
Liu S, Zhang YJ, Chen L, et al. Whole-lesion apparent diffusion coefficient histogram analysis: significance in T and N staging of gastric cancers[J]. BMC Cancer, 2017, 17(1): 665. DOI: 10.1186/s12885-017-3622-9.
[26]
Liu SL, Liu S, Ji CF, et al. Application of CT texture analysis in predicting histopathological characteristics of gastric cancers[J]. Eur Radiol, 2017, 27(12): 4951-4959. DOI: 10.1007/s00330-017-4881-1.
[27]
Yang J, Wu QY, Xu L, et al. Integrating tumor and nodal radiomics to predict lymph node metastasis in gastric cancer[J]. Radiother Oncol, 2020, 150: 89-96. DOI: 10.1016/j.radonc.2020.06.004.
[28]
Ba-Ssalamah A, Muin D, Schernthaner R, et al. Texture-based classification of different gastric tumors at contrast-enhanced CT[J]. Eur J Radiol, 2013, 82(10): e537-e543. DOI: 10.1016/j.ejrad.2013.06.024.
[29]
Giganti F, Marra P, Ambrosi A, et al. Pre-treatment MDCT-based texture analysis for therapy response prediction in gastric cancer: comparison with tumour regression grade at final histology[J]. Eur J Radiol, 2017, 90: 129-137. DOI: 10.1016/j.ejrad.2017.02.043.
[30]
Sun KY, Hu HT, Chen SL, et al. CT-based radiomics scores predict response to neoadjuvant chemotherapy and survival in patients with gastric cancer[J]. BMC Cancer, 2020, 20(1): 468. DOI: 10.1186/s12885-020-06970-7.
[31]
Amin MB, Greene FL, Edge SB, et al. The Eighth Edition AJCC Cancer Staging Manual: continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging[J]. CA Cancer J Clin, 2017, 67(2): 93-99. DOI: 10.3322/caac.21388.
[32]
In H, Solsky I, Palis B, et al. Validation of the 8th edition of the AJCC TNM staging system for gastric cancer using the national cancer database[J]. Ann Surg Oncol, 2017, 24(12): 3683-3691. DOI: 10.1245/s10434-017-6078-x.
[33]
Yardimci AH, Sel I, Bektas CT, et al. Computed tomography texture analysis in patients with gastric cancer: a quantitative imaging biomarker for preoperative evaluation before neoadjuvant chemotherapy treatment[J]. Jpn J Radiol, 2020, 38(6): 553-560. DOI: 10.1007/s11604-020-00936-2.
[34]
Wang Y, Liu W, Yu Y, et al. Prediction of the depth of tumor invasion in gastric cancer: potential role of CT radiomics[J]. Acad Radiol, 2020, 27(8): 1077-1084. DOI: 10.1016/j.acra.2019.10.020.
[35]
Wang LY, Zhang Y, Chen Y, et al. The performance of a dual-energy CT derived radiomics model in differentiating serosal invasion for advanced gastric cancer patients after neoadjuvant chemotherapy: iodine map combined with 120-kV equivalent mixed images[J]. Front Oncol, 2020, 10: 562945. DOI: 10.3389/fonc.2020.562945.
[36]
Pan BJ, Zhang WT, Chen WJ, et al. Establishment of the radiologic tumor invasion index based on radiomics splenic features and clinical factors to predict serous invasion of gastric cancer[J]. Front Oncol, 2021, 11: 682456. DOI: 10.3389/fonc.2021.682456.
[37]
Sun RJ, Fang MJ, Tang L, et al. CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer[J]. Eur J Radiol, 2020, 132: 109277. DOI: 10.1016/j.ejrad.2020.109277.
[38]
Liu SL, He J, Liu S, et al. Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer[J]. Eur Radiol, 2020, 30(1): 239-246. DOI: 10.1007/s00330-019-06368-5.
[39]
Kwee RM, Kwee TC. Imaging in local staging of gastric cancer: a systematic review[J]. J Clin Oncol, 2007, 25(15): 2107-2116. DOI: 10.1200/JCO.2006.09.5224.
[40]
Liu S, Zhang YJ, Xia J, et al. Predicting the nodal status in gastric cancers: the role of apparent diffusion coefficient histogram characteristic analysis[J]. Magn Reson Imaging, 2017, 42: 144-151. DOI: 10.1016/j.mri.2017.07.013.
[41]
Liu S, Zheng HH, Zhang YJ, et al. Whole-volume apparent diffusion coefficient-based entropy parameters for assessment of gastric cancer aggressiveness[J]. J Magn Reson Imaging, 2018, 47(1): 168-175. DOI: 10.1002/jmri.25752.
[42]
Chen WJ, Wang SW, Dong D, et al. Evaluation of lymph node metastasis in advanced gastric cancer using magnetic resonance imaging-based radiomics[J]. Front Oncol, 2019, 9: 1265. DOI: 10.3389/fonc.2019.01265.
[43]
Zhang YJ, Chen J, Liu S, et al. Assessment of histological differentiation in gastric cancers using whole-volume histogram analysis of apparent diffusion coefficient maps[J]. J Magn Reson Imaging, 2017, 45(2): 440-449. DOI: 10.1002/jmri.25360.
[44]
Wang XX, Ding Y, Wang SW, et al. Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer[J]. Cancer Imaging, 2020, 20(1): 83. DOI: 10.1186/s40644-020-00358-3.
[45]
Yoon SH, Kim YH, Lee YJ, et al. Tumor heterogeneity in human epidermal growth factor receptor 2 (HER2)-positive advanced gastric cancer assessed by CT texture analysis: association with survival after trastuzumab treatment[J]. PLoS One, 2016, 11(8): e0161278. DOI: 10.1371/journal.pone.0161278.
[46]
Wang N, Wang XX, Li WY, et al. Contrast-enhanced CT parameters of gastric adenocarcinoma: can radiomic features be surrogate biomarkers for HER2 over-expression status?[J]. Cancer Manag Res, 2020, 12: 1211-1219. DOI: 10.2147/CMAR.S230138.
[47]
Feng B, Huang LB, Li CL, et al. A heterogeneity radiomic nomogram for preoperative differentiation of primary gastric lymphoma from borrmann type IV gastric cancer[J]. J Comput Assist Tomogr, 2021, 45(2): 191-202. DOI: 10.1097/RCT.0000000000001117.
[48]
Sun YW, Ji CF, Wang H, et al. Differentiating gastric cancer and gastric lymphoma using texture analysis (TA) of positron emission tomography (PET)[J]. Chin Med J (Engl), 2020, 134(4): 439-447. DOI: 10.1097/CM9.0000000000001206.
[49]
Coccolini F, Nardi M, Montori G, et al. Neoadjuvant chemotherapy in advanced gastric and esophago-gastric cancer. Meta-analysis of randomized trials[J]. Int J Surg, 2018, 51: 120-127. DOI: 10.1016/j.ijsu.2018.01.008.
[50]
Kodera Y. Neoadjuvant chemotherapy for gastric adenocarcinoma in Japan[J]. Surg Today, 2017, 47(8): 899-907. DOI: 10.1007/s00595-017-1473-2.
[51]
Li ZH, Zhang DF, Dai YG, et al. Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: a pilot study[J]. Chin J Cancer Res, 2018, 30(4): 406-414. DOI: 10.21147/j.issn.1000-9604.2018.04.03.
[52]
Klaassen R, Larue RTHM, Mearadji B, et al. Feasibility of CT radiomics to predict treatment response of individual liver metastases in esophagogastric cancer patients[J]. PLoS One, 2018, 13(11): e0207362. DOI: 10.1371/journal.pone.0207362.
[53]
Jiang YM, Yuan QY, Lv WB, et al. Radiomic signature of 18F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits[J]. Theranostics, 2018, 8(21): 5915-5928. DOI: 10.7150/thno.28018.
[54]
Hou Z, Yang Y, Li SS, et al. Radiomic analysis using contrast-enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with abdominal cavity metastasis[J]. Quant Imaging Med Surg, 2018, 8(4): 410-420. DOI: 10.21037/qims.2018.05.01.
[55]
Giganti F, Antunes S, Salerno A, et al. Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker[J]. Eur Radiol, 2017, 27(5): 1831-1839. DOI: 10.1007/s00330-016-4540-y.
[56]
Jiang Y, Wang H, Wu J, et al. Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer[J]. Ann Oncol, 2020, 31(6): 760-768. DOI: 10.1016/j.annonc.2020.03.295.
[57]
Park JE, Kim D, Kim HS, et al. Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement[J]. Eur Radiol, 2020, 30(1): 523-536. DOI: 10.1007/s00330-019-06360-z.
[58]
Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. DOI: 10.1038/nrclinonc.2017.141.
[59]
Moons KG, Altman DG, Reitsma JB, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration[J]. Ann Intern Med, 2015, 162(1): W1-W73. DOI: 10.7326/M14-0698.
[60]
Shu Y, Zhang WH, Hou QQ, et al. Prognostic significance of frequent CLDN18-ARHGAP26/6 fusion in gastric signet-ring cell cancer[J]. Nat Commun, 2018, 9(1): 2447. DOI: 10.1038/s41467-018-04907-0.
[61]
Network CGAR. Comprehensive molecular characterization of gastric adenocarcinoma[J]. Nature, 2014, 513(7517): 202-209. DOI: 10.1038/nature13480.
[62]
Zhang WH, Zhang SY, Hou QQ, et al. The significance of the CLDN18-ARHGAP fusion gene in gastric cancer: a systematic review and meta-analysis[J]. Front Oncol, 2020, 10: 1214. DOI: 10.3389/fonc.2020.01214.
[63]
Huang AD, Zhang MY, Li TJ, et al. Serum proteomic analysis by tandem mass tags (TMT) based quantitative proteomics in gastric cancer patients[J]. Clin Lab, 2018, 64(5): 855-866. DOI: 10.7754/Clin.Lab.2018.171129.

PREV Application progress of hepatobiliary specific contrast agent MRI in diffuse disease of the liver
NEXT Development of artificial intelligence in diagnosis and treatment of spinal diseases
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn